Theory Of Genetic Programming And Its Application In Symbolic Regression

Regression analysis is one of the most important tasks in measurement data processing. The meaning of symbolic regression is that we are searching for a function that closely matches an unknown expression based on a finite set of sample data, in order to analyze and forecast data. Commonly used method is providing empirical expression model and estimating the regression parameters. The fitting results are bad when the function models are hard to be provided. Genetic programming can solve this problem because it can obtain the matched function expression by only given the data points and acceptable error.Genetic programming is a new technology for optimization, which simulates inheritance and evolution in the nature and gets optimal solutions through reproduction, crossover and mutation operations.In this thesis, the basic algorithm and theory are described firstly, and then the research state and advances at home and abroad are systematic summarized. While applying genetic programming in data fitting, the accurate symbolic regression results without expression model are achieved.For symbolic regression, with lots of experiment data, we get a reasonable and efficient parameter setting method, and provide a convenient...